Machu Picchu - The Part They DON'T Teach in School - Lost Ancient Technology
🛈⏬The true history of Ancient Human Civilization is far older, and was far more advanced than we were ever taught in school. There is significant evidence of lost ancient high technology at the site of Machu Picchu, located outside Cuzco, Peru - in the Peruvian Andes. Help support me on Patreon so I can make videos full-time! https://www.patreon.com/BrightInsight Be sure to check out the works of Brien Foerster - Video on Machu Picchu: https://youtu.be/zXlXmSkBpog Website: www.hiddenincatours.com/ YouTube: https://www.youtube.com/channel/UCOavg1FtdeuyUTLz3wmuIKQ1. Algorithmic Thinking, Peak Finding
🛈⏬MIT 6.006 Introduction to Algorithms, Fall 2011 View the complete course: http://ocw.mit.edu/6-006F11 Instructor: Srini Devadas License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.eduThe Future of Data Science - Data Science @ Stanford
🛈⏬Data science holds the potential to impact our lives and how we work dramatically. Despite its promise, many questions about data science remain. How real is this emerging discipline? What opportunities and challenges does it present? How can Stanford nurture data science in research and education? Watch the video and hear some of Stanford's thought leaders debate the answers to these questions.These Ancient Relics Are So Advanced They Shouldn't Exist...
🛈⏬First 500 people get a free 2 month trial of Skillshare http://skl.sh/thoughty3 JOIN The PRIVATE Thoughty2 Club & Get Exclusive Perks! http://bit.ly/t2club SUBSCRIBE - New Video Every Two Weeks http://bit.ly/thoughty2 BECOME A PATRON and support Thoughty2: https://www.patreon.com/thoughty2 Thoughty2 Merchandise: https://shop.thoughty2.com/ Thoughty2 Facebook: http://bit.ly/thoughtyfb Thoughty2 Instagram: http://bit.ly/t2insta Thanks to The Patreon Research Team: David Davenport, Michael Mulligan, Jeff Li, Anais Namahoro, NoaGoogle's Deep Mind Explained! - Self Learning A.I.
🛈⏬Subscribe here: https://goo.gl/9FS8uF Become a Patreon!: https://www.patreon.com/ColdFusion_TV Visual animal AI: https://www.youtube.com/watch?v=DgPaCWJL7XI Hi, welcome to ColdFusion (formally known as ColdfusTion). Experience the cutting edge of the world around us in a fun relaxed atmosphere. Sources: Why AlphaGo is NOT an Expert System : https://googleblog.blogspot.com.au/2016/01/alphago-machine-learning-game-go.html “Inside DeepMind” Nature video: https://www.youtube.com/watch?v=xN1d3qHMIEQ “AlphaGo and the future of Artificial Intelligence” BBC Newsnight: https://www.youtube.com/watch?v=53YLZBSS0cc http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html http://www.ft.com/cms/s/2/063c1176-d29a-11e5-969e-9d801cf5e15b.html http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html#tables https://www.technologyreview.com/s/533741/best-of-2014-googles-secretive-deepmind-startup-unveils-a-neural-turing-machine/ https://medium.com/the-physics-arxiv-blog/the-last-ai-breakthrough-deepmind-made-before-google-bought-it-for-400m-7952031ee5e1 https://www.deepmind.com/ www.forbes.com/sites/privacynotice/2014/02/03/inside-googles-mysterious-ethics-board/#5dc388ee4674 https://medium.com/the-physics-arxiv-blog/the-last-ai-breakthrough-deepmind-made-before-google-bought-it-for-400m-7952031ee5e1#.4yt5o1e59 http://www.theverge.com/2016/3/10/11192774/demis-hassabis-interview-alphago-google-deepmind-ai https://en.wikipedia.org/wiki/Demis_Hassabis https://en.wikipedia.org/wiki/Google_DeepMind //Soundtrack// Disclosure - You & Me (Ft. Eliza Doolittle) (Bicep Remix) Stumbleine - Glacier Sundra - Drifting in the Sea of Dreams (Chapter 2) Dakent - Noon (Mindthings Rework) Hnrk - fjarlæg Dr Meaker - Don't Think It's Love (Real Connoisseur Remix) Sweetheart of Kairi - Last Summer Song (ft. CoMa) Hiatus - Nimbus KOAN Sound & Asa - This Time Around (feat. Koo) Burn Water - Hide » Google + | http://www.google.com/+coldfustion » Facebook | https://www.facebook.com/ColdFusionTV » My music | t.guarva.com.au/BurnWater http://burnwater.bandcamp.com or » http://www.soundcloud.com/burnwater » https://www.patreon.com/ColdFusion_TV » Collection of music used in videos: https://www.youtube.com/watch?v=YOrJJKW31OA Producer: Dagogo Altraide Editing website: www.cfnstudios.com Coldfusion Android Launcher: https://play.google.com/store/apps/details?id=nqr.coldfustion.com&hl=en » Twitter | @ColdFusion_TVAccelerated Learning: Gamma Waves for Focus, Concentration, Memory - Monaural Beats #1934
🛈⏬Accelerated Learning: Gamma Waves for Focus, Concentration, Memory - Monaural Beats #1934 Purchase this HD MP3: https://goo.gl/Jpk2zw Magnetic Minds: This video contains 40 Hz Gamma Monaural Beats. Gamma Waves are associated with Higher Awareness States, such as those seen during intensive task processing, or Tibetan monks meditating on the intent of compassion. Frequency Information: 40 Hz Gamma Monaural Beats Intellectual Acuity 6 Hz Theta Binaural Beats Memory Stimulation Carrier Frequency: 126.1 Hz (Hyper-Gamma) If you enjoy this video, please Like and Subscribe for weekly updates. ===== General Questions ===== Q. What are Binaural Beats? Binaural Beats is a term given to playing one sound frequency in one ear, and another sound frequency in the opposite ear, creating a two-tone effect in the mid-brain that is actually perceived to be one tone. This causes an Entrainment effect in the brain that has a variety of results depending on the frequency. Q. What are Binaural Beats good for? Lots of things. Meditation, Relaxation, Stress Relief, Deeper Sleep, Pain Relief, Mind Expansion, Brain Hemisphere Synchronization, and the list goes on and on. Pretty much any element of the Mind / Body complex can be improved using Binaural Beats, which again is just Brainwave Entrainment. Q. Do Binaural Beats Actually Work? Indeed. Many scientific studies (especially as of late) have conclusive research on Brainwave Entrainment and it's effects. Q. Must I wear headphones for these videos? You don't have to use headphones, but the Binaural effect is increased if you do. Q. Do I need to close my eyes while listening to this? No, although you'll find closing your eyes will generally lead to a deeper, more profound state while listening. If you enjoy this video, please Like and Subscribe for weekly updates.Random Forests - The Math of Intelligence (Week 6)
🛈⏬This is one of the most used machine learning models ever. Random Forests can be used for both regression and classification, and our use case will be to assess whether someone is credible or not by analyzing their financial history! DL nanodegree open for another round! we'll pick one random student that signs up in next 24 hrs to collab w/ me one-on-one on a DL music project https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101 Code for this video: https://github.com/llSourcell/random_forests Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: https://ujjwalkarn.me/2016/05/30/a-curated-list-of-python-tutorials-for-data-science-nlp-and-machine-learning/ https://www.coursera.org/learn/machine-learning-data-analysis/lecture/eTO92/building-a-random-forest-with-python https://github.com/kevin-keraudren/randomforest-python http://kldavenport.com/pure-python-decision-trees/ http://blog.yhat.com/posts/random-forests-in-python.html https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial-tree-based-modeling-scratch-in-python/ http://machinelearningmastery.com/implement-decision-tree-algorithm-scratch-python/ http://machinelearningmastery.com/implement-random-forest-scratch-python/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5wHow to become a Data Scientist
🛈⏬Becoming a Data Scientist - This video will give you a broad prospective of data scientist career path that include learning to love number crunching, developing skills in algebra, statistics, and machine learning, and working on interesting data science projects. Also, check out - How to become a Data Scientist here - https://goo.gl/vud3J1 Want to learn Data Science with Python then check out , Data Scientist Certification Training Online here - https://goo.gl/b9nqvSTop 5 programming language in 2019 with Learning Paths
🛈⏬In this video, I would like to put up my compiled list of Top programming language. This video is divided in 4 sections. 1. An Amazon interview experience 2. Reasons and factors for this list 3. Actual list of languages with learning paths and projects 4. A quick talk over Data Structure and Algorithms If you are just interested in list: 5. PHP 4. SWIFT 3. Java 2. JavaScript 1. Python but this video is not just about list, experience and learning path is also included. For php, first start with Front end web development: https://courses.learncodeonline.in/learn/complete-front-end-development For Swift start here: https://courses.learncodeonline.in/learn/Complete-iOS-11-developer-Bootcamp For Java, start with Java Bootcamp https://courses.learncodeonline.in/learn/Complete-Java-Bootcamp and then Android P with 26+ apps: https://courses.learncodeonline.in/learn/Android-P-with-Machine-learning-Apps For Javascript, here is a free course: https://courses.learncodeonline.in/learn/Javascript-for-2018-developer Python can lead you with Django, web development here: https://courses.learncodeonline.in/learn/BackEnd-web-development-with-Django or Machine Learning guide here: https://courses.learncodeonline.in/learn/Machine-Learning-Bootcamp These are some online resource, that I have created over years. Here is the mentioned book in video: https://amzn.to/2A1B0Ak Link to my programming Video Library: https://courses.LearnCodeOnline.in Pick best UI color for your projects: https://UIColorPicker.com Desktop: https://amzn.to/2GZ0C46 Laptop that I use: https://amzn.to/2Goui9Q Wallpaper: https://imgur.com/a/FYHfk Facebook: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com Download LearnCodeOnline.in app from Google play store and Apple App store Disclaimer: It doesn't feel good to have a disclaimer in every video but this is how the world is right now. All videos are for educational purpose and use them wisely. Any video may have a slight mistake, please take decisions based on your research. This video is not forcing anything on you. All Amazon links are affiliate links (If any).-Statistics and Big Data at Google-
🛈⏬Tim Hesterberg, Senior Statistician, GoogleTalking Tech with Elon Musk!
🛈⏬Talking Tesla, tech and the future with Elon Musk. MKBHD Merch: http://shop.MKBHD.com Video Gear I use: http://kit.com/MKBHD/video-gear#recommendation17959 Tech I'm using right now: https://www.amazon.com/shop/MKBHD Intro Track: Long Walk off a Short Pier by deadmau5 ~ http://twitter.com/MKBHD http://snapchat.com/add/MKBHD http://google.com/+MarquesBrownlee http://instagram.com/MKBHD http://facebook.com/MKBHDGaussian Mixture Models - The Math of Intelligence (Week 7)
🛈⏬We're going to predict customer churn using a clustering technique called the Gaussian Mixture Model! This is a probability distribution that consists of multiple Gaussian distributions, very cool. I also have something important but unrelated to say in the beginning of the video. Code for this video: https://github.com/llSourcell/Gaussian_Mixture_Models Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: http://yulearning.blogspot.nl/2014/11/einsteins-most-famous-equation-is-emc2.html http://web.iitd.ac.in/~sumeet/GMM_said_crv10_tutorial.pdf https://brilliant.org/wiki/gaussian-mixture-model/ http://www.vlfeat.org/overview/gmm.html http://www.informatica.uniroma2.it/upload/2009/IM/mixture-tutorial.pdf http://cs.nyu.edu/~dsontag/courses/ml12/slides/lecture21.pdf http://statweb.stanford.edu/~tibs/stat315a/LECTURES/em.pdf Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5wSay NO to NoSQL in 2018!
🛈⏬So what is noSQL? From Wikipedia: A NoSQL (originally referring to non SQL or non relational )[1] database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. https://en.wikipedia.org/wiki/NoSQL In this video, I explain why for 99% of projects, you are probably better off with an SQL database. The need for sharded (sharding) databases, and horizontal scaling (one of the mentioned key advantages of noSQL databases) is exceptionally rare. My popular courses: Complete Freelancer: https://www.killervideostore.com/freelancer Learn web development fast: https://shop.killervideostore.com/ Learn Python 3 fast: http://www.killervideostore.com/python/ Complete Entrepreneur Course: https://goo.gl/kpVUD2 My social links: Instagram: https://www.instagram.com/stefanmischook/?hl=en Twitter: https://twitter.com/killersites Thanks! Stef #Need2Nerd #NoSQL #RDMSData Science - Part I - Building Predictive Analytics Capabilities
🛈⏬For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This is the first video lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.What is OAuth2? How does OAuth2 work? | Tech Primers
🛈⏬This video covers the basics about OAuth and How OAuth 2 works? OAuth2 Website: https://oauth.net/2/ Twitter: https://twitter.com/TechPrimers Facebook: http://fb.me/TechPrimers GitHub: https://github.com/TechPrimers or https://techprimers.github.io/ Video Editing: iMovie Intro Music: A Way for me (www.hooksounds.com) #Security #OAuth2 #TechPrimersWhat it's Like to Interview as a Data Scientist
🛈⏬This is my first video ever about interviewing as a Data Scientist. I'm not very good at speaking in front of a camera but I hope to get better. I mainly wanted to make this video for everyone interviewing for data science jobs. Here are a few links to help brush up on implementing data structures: https://www.hackerrank.com/ https://codility.com/programmers/lessons/ http://www.geeksforgeeks.org/fundamentals-of-algorithms/ https://www.codecademy.com/ https://www.topcoder.com/ https://www.codechef.com/ Study material online: https://www.khanacademy.org/computing/computer-science/algorithms https://www.coursera.org/course/algs4partI https://www.coursera.org/course/algs4partII https://www.coursera.org/course/algo https://www.coursera.org/course/algo2 Great Python specific online book /w code samples: http://interactivepython.org/runestone/static/pythonds/index.html Connect with me on LinkedIn: https://linkedin.com/in/davidyerringtonRandom Forest - Fun and Easy Machine Learning
🛈⏬Random Forest - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Hey Guys, and welcome to another Fun and Easy Machine Learning Algorithm on Random Forests. Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. In general, the more trees in the forest the more robust the prediction. In the same way in the random forest classifier, the higher the number of trees in the forest gives the high accuracy results. To model multiple decision trees to create the forest you are not going to use the same method of constructing the decision with information gain or gini index approach, amongst other algorithms. If you are not aware of the concepts of decision tree classifier, Please check out my lecture here on Decision Tree CART for Machine learning. You will need to know how the decision tree classifier works before you can learn the working nature of the random forest algorithm. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)Algorithms Are Taking Over The World: Christopher Steiner at TEDxOrangeCoast
🛈⏬Christopher Steiner is the author of Automate This (2012) and $20 Per Gallon, a New York Times Bestseller (2009). He is a cofounder at Aisle50, a Y Combinator company that sells grocery deals through the Web. Before starting Aisle50 in 2011, Steiner was a senior writer covering technology at Forbes magazine for seven years. His writing has also appeared in The Wall Street Journal, the Chicago Tribune, Fast Company, MIT Technology Review and Skiing Magazine. He holds an engineering degree from the University of Illinois at Urbana-Champaign and a masters in journalism from Northwestern University. Steiner lives in Evanston, Ill., with his family. About TEDx. TEDx was created in the spirit of TED's mission, ideas worth spreading. The program is designed to give communities, organizations and individuals the opportunity to stimulate dialogue through TED-like experiences at the local level. At TEDx events, a screening of TEDTalks videos -- or a combination of live presenters and TEDTalks videos -- sparks deep conversation and connections. TEDx events are fully planned and coordinated independently, on a community-by-community basisData Science Crash Course
🛈⏬Here are updated, more compact videos: Overview of Basic ML Algorithms: www.youtube.com/watch?v=ggIk08PNcBo Overview of Spark MLlib Module: www.youtube.com/watch?v=C-44fsv5XgE Click this link to view slides. http://www.slideshare.net/HadoopSummit/data-science-with-apache-spark-crash-course-hs16sjWhat Are the Top Skills Needed to Be a Data Scientist?
🛈⏬https://www.sas.com/en_us/learn/academy-data-science.html Dr. Goutam Chakraborty of Oklahoma State University outlines the top skills you need to be successful as a data scientist. SAS ACADEMY FOR DATA SCIENCE Analytical talent is in high demand. Differentiate yourself by earning your certification in big data and data science from SAS, the world leader in advanced big data analytics. The SAS Academy for Data Science can help you sharpen your skills and validate your expertise – for employers, customers and yourself. Learn more! https://www.sas.com/en_us/learn/academy-data-science.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rssCode Learning Strategies that WORK WONDERS!
🛈⏬Learn web development fast: https://shop.killervideostore.com/ Learn business: https://goo.gl/QF5v4o Learn Python 3 fast: http://www.killervideostore.com/python/ There is a right way to learn, and a wrong way ... especially when it comes to programming! The key to quickly learning to code comes down to how our brains actually work, that's where my psych background comes into play. Some quick learning tips for new coders: 1. Be consistent over time. 2. It's important to give your mind some time to rest and assimilate what it was exposed to - the code. Join a community of nerds: https://www.killersites.com/community/ My Instagram: https://www.instagram.com/stefanmischook/?hl=en Thanks! StefTutorial: Applied Data Science in Python
🛈⏬Tennessee Leeuwenburg https://linux.conf.au/schedule/30020/view_talk Ever tried to get into data science or machine learning, but struggled with getting your tech stack working, or found the maths off-putting? Curious about the limits of what your laptop or desktop really are when it comes to Big Data and predictive analytics? Ever wondered if these tools were really accessible to a general developer? This tutorial will provide attendees with a walkthrough on getting set up for this work, and an overview of a good tech stack / software ecosystem for beginning work. We'll cover some of the standard data sets in machine learning, and how to apply interesting algorithms. Random Forests and neural networks will be included, but with a minimum of fuss and jargon. There will be a focus on the application of technology, with only the most relevant theoretical aspects included. This is about actually getting things done. This tutorial would be suitable for intermediate developers of any background, or experienced developers who would like an introduction to data science that gets to the point fast. Prerequisites: the ability to install Python modules on your laptop, the ability to set up a new virtual environment, and an interest in applying new techniques. The tutorial will include clear walkthroughs, as well as allowing adequate time for discussion and individual learning. Please contact Tennessee via email ahead of time if you would like to get a head start on setting up your environment -- this may help you get more out of the tutorial.Top 5 Algorithms used in Data Science | Data Science Tutorial | Data Mining Tutorial | Edureka
🛈⏬( Data Science Training - https://www.edureka.co/data-science ) This tutorial will give you an overview of the most common algorithms that are used in Data Science. Here, you will learn what activities Data Scientists do and you will learn how they use algorithms like Decision Tree, Random Forest, Association Rule Mining, Linear Regression and K-Means Clustering. To learn more about Data Science click here: http://goo.gl/9HsPlv The topics related to 'R', Machine learning and Hadoop and various other algorithms have been extensively covered in our course “Data Science”. For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edurekaAwesome Big Data Algorithms
🛈⏬Titus Brown Random algorithms and probabilistic data structures are algorithmically efficient and can provide shockingly good practical results. I will give a practical introduction, with live demos and bad jokes, to this fascinating algorithmic nicLife as a Data Scientist at Adobe
🛈⏬Adobe is a powerhouse in the digital marketing space and it has attracted some of the world’s brightest data scientists to analyze more than 35 petabytes of customer data that it manages for Fortune 500 companies and more. These data scientists unlock trends behind enormous amounts of data to help companies better serve their customers. At Adobe, data scientists get to play in the theoretical world of academia, apply theories to real customer problems and see their theories come to life in products. Interested in a career as a data scientist at Adobe? Visit our career site at www.adobe.com/careers to explore exciting opportunities on the team.Applications of Weight of Evidence and Information values:Predictive Modelling
🛈⏬In this video you will learn the purpose of doing Weight of Evidence Transformation and use of Information Value criteria for variable reduction For Training & Study packs on Analytics/Data Science/Big Data, Contact us at analyticsuniversity@gmail.com Find all free videos & study packs available with us here: http://analyticsuniversityblog.blogspot.in/ SUBSCRIBE TO THIS CHANNEL for free tutorials on Analytics/Data Science/Big Data/SAS/R/HadoopHow to learn data science
🛈⏬A session by Vik Paruchuri, founder of dataquest.io and self-taught data scientist, on how to learn data science. We'll do a short presentation on some of the best ways to learn, and then take questions.Decision Tree with R | Complete Example
🛈⏬Also called Classification and Regression Trees (CART) or just trees. R file: https://goo.gl/Kx4EsU Data file: https://goo.gl/gAQTx4 Includes, - Illustrates the process using cardiotocographic data - Decision tree and interpretation with party package - Decision tree and interpretation with rpart package - Plot with rpart.plot - Prediction for validation dataset based on model build using training dataset - Calculation of misclassification error Decision trees are an important tool for developing classification or predictive analytics models related to analyzing big data or data science. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.Data Science Interview Questions | Data Science Tutorial | Data Science Interviews | Edureka
🛈⏬( Data Science Training - https://www.edureka.co/data-science ) This Data Science Interview Questions and Answers video will help you to prepare yourself for Data Science and Big Data Analytics interviews. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science, Big Data Analytics and Machine Learning. Below are the topics covered in this tutorial: 1. Data Science Job Trends 2. Data Science Interview Questions A. Statistics Questions B. Data Analytics Questions C. Machine Learning Questions D. Probability Questions 3. Conclusion Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #DataScienceInterviewQuestions #BigDataAnalytics #DataScienceTutorial #DataScienceTraining #Datascience #Edureka How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. Dimensionality Reduction - The Math of Intelligence #5
🛈⏬Most of the datasets you'll find will have more than 3 dimensions. How are you supposed to understand visualize n-dimensional data? Enter dimensionality reduction techniques. We'll go over the the math behind the most popular such technique called Principal Component Analysis. Code for this video: https://github.com/llSourcell/Dimensionality_Reduction Ong's Winning Code: https://github.com/jrios6/Math-of-Intelligence/tree/master/4-Self-Organizing-Maps Hammad's Runner up Code: https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Self%20Organizing%20Maps%20for%20Data%20Visualization Please Subscribe! And like. And comment. That's what keeps me going. I used a screengrab from 3blue1brown's awesome videos: https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw More learning resources: https://plot.ly/ipython-notebooks/principal-component-analysis/ https://www.youtube.com/watch?v=lrHboFMio7g https://www.dezyre.com/data-science-in-python-tutorial/principal-component-analysis-tutorial https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/ http://setosa.io/ev/principal-component-analysis/ http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html https://algobeans.com/2016/06/15/principal-component-analysis-tutorial/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5wHow to Become a Data Scientist in 2017? | Data Scientist Career | Data Science Future
🛈⏬About the Webinar : Agenda of this session will include answers to the following questions: 1. Why is it the best time to take up Data Science as a career? 2. How can you take the first step in Data Science? (After all, first step is always the hardest!) 3. How can you become better and progress fast? 4. How is life after becoming a Data Scientist? About the Host : Jesse Steinweg-Woods is soon-to-be a Senior Data Scientist at tronc, working on recommender systems for articles and understanding customer behavior. Previously, he worked at Argo Group Insurance on new pricing models that took advantage of machine learning techniques. He received his PhD in Atmospheric Science from Texas A&M University, and his research focused on numerical weather and climate prediction. Slides: https://www.slideshare.net/HackerEarth/how-to-become-a-data-scientist-72258005 Questions Answered: 1. How can a college fresher (say, studying in sophomore or final year) become a data scientist ? What projects can they do ? What skills should they focus on ? How to start applying for jobs ? 2. How can an experienced professional make a career shift into data science ? Let's say, someone has 3 years of experience in Java, and he now wants to become a data scientist. Or, let's say, someone knows Hive, Pig, Flume, Hadoop, what could be natural career progression for him ? 3. How is a Machine Learning Engineer different from Data Scientist ? 4. How is a Statistician different from a Data Scientist ? 5. How is a Data Engineer different from a Data Scientist ? 6. What is the relationship between data science and machine learning ? 7. What are the most commonly used ML algorithms in industry today, so that students can master them first ? 8. What is the future of Data Scientist job ? Will it survive after 5 - 10 years or get automated? 9. Can data science be used in building geological applications ? If yes, what would be the starting point ? More webinars and updates : https://goo.gl/MEAALs Subscribe our channel for More Updates : https://goo.gl/suzeTBMachine Learning Algorithms | Machine Learning Tutorial | Data Science Algorithms | Simplilearn
🛈⏬This Machine Learning Algorithms Tutorial video will help you learn you what is Machine Learning, various Machine Learning problems and the algorithms, key Machine Learning algorithms with simple examples and use cases implemented in Python. The key Machine Learning algorithms discussed in detail are Linear Regression, Logistic Regression, Decision Tree, Random Forest and KNN algorithm. This Machine Learning Algorithms tutorial is designed for beginners to understand which algorithm to use when, how each algorithm works and implement it on Python with real-life use cases. Below topics are covered in this Machine Learning Algorithms Tutorial: 1. Real world applications of Machine Learning 2. What is Machine Learning? 3. Processes involved in Machine Learning 4. Type of Machine Learning Algorithms 5. Popular Algorithms with hands-on demo - Linear regression - Logistic regression - Decision tree and Random forest - N Nearest neighbor What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Machine-Learning-Algorithms-I7NrVwm3apg&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Machine-Learning-Algorithms-I7NrVwm3apg&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0Data Science and Statistics: different worlds?
🛈⏬Chris Wiggins (Chief Data Scientist, New York Times) David Hand (Emeritus Professor of Mathematics, Imperial College) Francine Bennett (Founder, Mastodon-C) Patrick Wolfe (Professor of Statistics, UCL / Executive Director, UCL Big Data Institute) Zoubin Ghahramani (Professor of Machine Learning, University of Cambridge) Chair: Martin Goodson (Vice-President Data Science, Skimlinks) Discussant: John Pullinger (UK National Statistician) In the last few years data science has become an increasingly popular discipline. Often linked to the use and analysis of ‘big data’, data scientists are seen as the new professionals who can unlock the potential of an increasingly data-rich world, and to generate economic and social benefits from the data revolution. However within the world of statistics, the ‘big data’ and ‘data scientist’ developments are sometimes labelled as hypes, and ‘data science’ is seen as a rebranding of what should be statistics. One of the often heard criticisms of big data analytics is that there’s a lack of statistical rigour which can lead to the wrong decisions. As with any new discipline there are questions about exactly what data science is. Has the relevance of statistics been diminished because of new types of data or technologies which need a radical new approach? Is data science about ‘getting the job done’, and statistics about the deeper scientific understanding? Are our universities offering students the right skill sets to meet the high demand for data scientists?Eight Data Science Algorithms | Data Analytics
🛈⏬In this video, you will be introduced to eight very important data science algorithms used by data scientists on daily basis Contact us : analyticsuniversity@gmail.comWhat is machine learning and how to learn it ?
🛈⏬http://www.LearnCodeOnline.in Machine learning is just to give trained data to a program and get better result for complex problems. It is very close to data mining. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today. fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.comQ&A on Decision Tree Modelling | Data Science Interview
🛈⏬In this video you will learn about the frequently asked questions in decision tree modelling. The questions you can expect could be on comparison between decision tree & regression , pruning . contact - analyticsuniversity@gmail.com ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOAAlgorithms for Big Data (COMPSCI 229r), Lecture 1
🛈⏬Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' algorithm.Introduction of Python in Data Science
🛈⏬Python is a powerful and beautiful programming language. It is widely used in data science for processing data and building learning models. In this talk, I will briefly introduce the very basics of Python, and quickly jump into a real analytic case to demonstrate how to use Python in data collection, processing, graph analysis, visualization, and building regression and classification models.What is Data Science? | Introduction to Data Science | Data Science for Beginners | Simplilearn
🛈⏬This Data Science tutorial will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about. This Data Science tutorial will cover the following topics: 1. Need for Data Science? ( 00:50 ) 2. What is Data Science? ( 05:55 ) 3. Data Science vs Business intelligence ( 11:44 ) 4. Prerequisites for learning Data Science ( 16:36 ) 5. What does a Data scientist do? ( 24:31 ) 6. Data Science life cycle with use case ( 30:17 ) 7. Demand for Data scientists ( 47:17 ) To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slide here: https://goo.gl/3d2pNv Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-KxryzSO1Fjs&utm_medium=Tutorials&utm_source=youtube Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course. Why learn Data Science? Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data. You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to: 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package The Data Science with python is recommended for: 1. Analytics professionals who want to work with Python 2. Software professionals looking to get into the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in analytics and data science 5. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=What-is-Data-Science-KxryzSO1Fjs&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0Why Data Scientist is The Best Job Of 2018 | Simplilearn
🛈⏬“Data Scientist” is the pinnacle rank in an analytics organization. Glassdoor has ranked Data Scientist first in the 25 Best Jobs for 2016. Needless to say, good data scientists are scarce and in great demand. As a data scientist you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019 Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709 Randstand reports that pay hikes in the analytics industry is 50% more than IT Become a Data Scientist today: http://www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training?utm_campaign=Why-Data-Scientist-Is-The-Best-Job-Of-2016-htNN-RtFb1Q-A&utm_medium=SC&utm_source=youtube For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0Learn Machine Learning in 3 Months (with curriculum)
🛈⏬How is a total beginner supposed to get started learning machine learning? I'm going to describe a 3 month curriculum to help you go from beginner to well-versed in machine learning. Its an accelerated learning plan, something i'd create for myself if I were to get started today, but I'm going to open source it for you guys. This curriculum will cover all the math concepts, the machine learning theory, and the deep learning theory to get you up to speed with the field as fast as possible. If anyone asks how to best get started with machine learning, direct them to this video! Curriculum from this video: https://github.com/llSourcell/Learn_Machine_Learning_in_3_Months Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Month 1 Week 1 Linear Algebra https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/ Week 2 Calculus https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr Week 3 https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2 Week 4 Algorithms https://www.coursera.org/courses?languages=en&query=Algorithm%20design%20and%20analysis Month 2 Week 1 learn python for data science https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU Math of Intelligence https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D Intro to Tensorflow https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV Week 2 Intro to ML (Udacity) https://eu.udacity.com/course/intro-to-machine-learning--ud120 Week 3-4 ML Project Ideas https://github.com/NirantK/awesome-project-ideas Month 3 (Deep Learning) Week 1 Intro to Deep Learning https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 Week 2 Deep Learning by Fast.AI http://course.fast.ai/ Week 3-4 Re-implement DL projects from my github https://github.com/llSourcell?tab=repositories ML people to follow on Twitter: https://www.quora.com/Who-should-I-follow-on-Twitter-to-get-useful-and-reliable-machine-learning-information Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5wTechnology for the Greater Good: Careers in Data Science
🛈⏬http://www.ischool.berkeley.edu/newsandevents/events/20140903careersnindatascience Wednesday, September 3, 2014, South Hall, UC Berkeley Bayes Impact is a non-profit organization deploying data science teams to work with civic and nonprofit organizations to solve big social impact challenges. They’ll present a panel of women data scientists who will discuss their career trajectories in the emerging and rapidly evolving field of data science. Panelists: * Katharine Matsumoto, Data Scientist in Product Intelligence, Salesforce.com * Vesela Gateva, Sr. Data Scientist, Eventbrite * Emi Nomura, Data Scientist, Jawbone * Elena Grewal, Data Scientist, Airbnb * Pinar Donmez, Chief Data Scientist, Kabbage, Inc * Anno Saxenian, Dean, School of Information (moderator)Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science Training | Edureka
🛈⏬( Data Science Training - https://www.edureka.co/data-science ) This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial: 1) Introduction to Classification 2) Why Random Forest? 3) What is Random Forest? 4) Random Forest Use Cases 5) How Random Forest Works? 6) Demo in R: Diabetes Prevention Use Case Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #RandomForest #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn
🛈⏬This Linear Regression in Machine Learning video will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it needed and how Simple Linear Regression works with solved examples, Linear regression analysis, applications of Linear Regression and Multiple Linear Regression model. At the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. This Machine Learning tutorial is ideal for beginners who want to understand Data Science algorithms as well as Machine Learning algorithms. Below topics are covered in this Linear Regression Machine Learning Tutorial: 1. Introduction to Machine Learning 2. Machine Learning Algorithms 3. Applications of Linear Regression 4. Understanding Linear Regression 5. Multiple Linear Regression 6. Usecase - Profit estimation of companies What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Linear-Regression-NUXdtN1W1FE&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Linear-Regression-NUXdtN1W1FE&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka
🛈⏬** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training ** This Edureka video on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial: 1. What is Classification? 2. Types of Classification 3. Classification Use Case 4. What is Decision Tree? 5. Decision Tree Terminology 6. Visualizing a Decision Tree 7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm Subscribe to our channel to get video updates. Hit the subscribe button above. Check out our Python Machine Learning Playlist: https://goo.gl/UxjTxm #decisiontree #decisiontreepython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edurekaKNN Algorithm using Python | How KNN Algorithm works | Python Data Science Training | Edureka
🛈⏬** Python for Data Science: https://www.edureka.co/python ** This Edureka video on KNN Algorithm will help you to build your base by covering the theoretical, mathematical and implementation part of the KNN algorithm in Python. Topics covered under this video includes: 1. What is KNN Algorithm? 2. Industrial Use case of KNN Algorithm 3. How things are predicted using KNN Algorithm 4. How to choose the value of K? 5. KNN Algorithm Using Python 6. Implementation of KNN Algorithm from scratch Check out our playlist for more videos: http://bit.ly/2taym8X Subscribe to our channel to get video updates. Hit the subscribe button above. #KNNAlgorithm #MachineLearningUsingPython #MachineLearningTraining How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edurekaData Science - Part III - EDA & Model Selection
🛈⏬For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture introduces the concept of EDA, understanding, and working with data for machine learning and predictive analysis. The lecture is designed for anyone who wants to understand how to work with data and does not get into the mathematics. We will discuss how to utilize summary statistics, diagnostic plots, data transformations, variable selection techniques including principal component analysis, and finally get into the concept of model selection.Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hadoop Training | Edureka
🛈⏬** Flat 20% Off (Use Code: YOUTUBE20) Hadoop Training: https://www.edureka.co/hadoop ** This Edureka Big Data tutorial ( Big Data Hadoop Blog series: https://goo.gl/LFesy8 ) helps you to understand Big Data in detail. This tutorial will be discussing about evolution of Big Data, factors associated with Big Data, different opportunities in Big Data. Further it will discuss about problems associated with Big Data and how Hadoop emerged as a solution. Below are the topics covered in this tutorial: 1) Evolution of Data 2) What is Big Data? 3) Big Data as an Opportunity 4) Problems in Encasing Big Data Opportunity 5) Hadoop as a Solution 6) Hadoop Ecosystem 7) Edureka Big Data & Hadoop Training Subscribe to our channel to get video updates. Hit the subscribe button above. Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Check our complete Hadoop playlist here: https://goo.gl/hzUO0m - - - - - - - - - - - - - - How it Works? 1. This is a 5 Week Instructor led Online Course, 40 hours of assignment and 30 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka’s Big Data and Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert Hadoop instructors will help you: 1. Master the concepts of HDFS and MapReduce framework 2. Understand Hadoop 2.x Architecture 3. Setup Hadoop Cluster and write Complex MapReduce programs 4. Learn data loading techniques using Sqoop and Flume 5. Perform data analytics using Pig, Hive and YARN 6. Implement HBase and MapReduce integration 7. Implement Advanced Usage and Indexing 8. Schedule jobs using Oozie 9. Implement best practices for Hadoop development 10. Work on a real life Project on Big Data Analytics 11. Understand Spark and its Ecosystem 12. Learn how to work in RDD in Spark - - - - - - - - - - - - - - Who should go for this course? If you belong to any of the following groups, knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career: 1. Analytics professionals 2. BI /ETL/DW professionals 3. Project managers 4. Testing professionals 5. Mainframe professionals 6. Software developers and architects 7. Recent graduates passionate about building successful career in Big Data - - - - - - - - - - - - - - Why Learn Hadoop? Big Data! A Worldwide Problem? According to Wikipedia, Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, it is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success! The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data. - - - - - - - - - - - - - - Opportunities for Hadoopers! Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself! For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free). Customer Review: Michael Harkins, System Architect, Hortonworks says: “The courses are top rate. The best part is live instruction, with playback. But my favourite feature is viewing a previous class. Also, they are always there to answer questions, and prompt when you open an issue if you are having any trouble. Added bonus ~ you get lifetime access to the course you took!!! ~ This is the killer education app... I've take two courses, and I'm taking two more.”Data Science With Python | Python for Data Science | Python Data Science Tutorial | Simplilearn
🛈⏬This Data Science with Python Tutorial will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis. This Data Science with Python tutorial will cover the following topics: 1. What is Data Science? 2. Basics of Python for data analysis - Why learn Python? - How to install Python? 3. Python libraries for data analysis 4. Exploratory analysis using Pandas - Introduction to series and dataframe - Loan prediction problem 5. Data wrangling using Pandas 6. Building a predictive model using Scikit-learn - Logistic regression To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/ifQRpS Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-bTTxei-S1WI&utm_medium=Tutorials&utm_source=youtube Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course. Why learn Data Science? Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data. You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to: 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package The Data Science with python is recommended for: 1. Analytics professionals who want to work with Python 2. Software professionals looking to get into the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in analytics and data science 5. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Data-Science-With-Python-mkv5mxYu0Wk&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0Brian Machine Learning vs Traditional Statistics Part 1
🛈⏬A lecture from the Data Science in Real Life Coursera Class. Part of the Coursera Executive Data Science Specialization.